PROCESS FOR CHARACTERIZING NEURAL NETWORKS BY PREDOMINANT INPUTS FOR IMPROVED VEHICLE FUNCTIONAL SAFETY

A functional safety calibration method for a vehicle includes accessing dynamometer data for the vehicle and determining, using the dynamometer data, first output data and N input parameters for a neural network utilized by a primary process of the vehicle to model a vehicle parameter based on the N input parameters, wherein N is an integer greater than one, identifying, based on the first output data, M of the N input parameters that predominantly affect the modeling of the vehicle parameter by the neural network, wherein M is an integer that is less than N, determining a function of at least M identified input parameters based on the first output data, and calibrating a secondary process using the determined function, wherein the secondary process is configured to be utilized by the vehicle as a functional safety check for the primary process.